The economics of poultry meat production depends on numerous factors, but most important is
general economic policy. For the economic analyses, net profit, gross return, net return, Benefit to Cost Ratio
(BCR), productivity, etc. have to be computed. In this study, various Artificial Neural Network (ANN) models
were developed to estimate the BCR of broiler farms in tropical regions of Iran. To develop ANN models, data
were obtained from growers, government officials as well as from relevant databases. The developed ANN was
a Multilayer Feed Forward Network (MLFN) with five neurons in the input layer, one and two hidden layer(s)
of various numbers of neurons and one neuron in the output layer. The MLFN were trained with the
experimental data obtained from 44 broiler farms. Based on performance measures, (5-20-1)-MLFN, namely,
a network having five neurons in its input layer and twenty neurons in the hidden layer resulted in the bestsuited
model estimating the BCR. For the optimal model, the values of the modelís outputs correlated well with
actual outputs, with coefficient of determination (R2) of 0.978. For this configuration, MSE, MAE and MAPE
values were 0.002, 0.037 and 2.695, respectively. Sensitivity analysis revealed that feed cost is the most
significant parameter in modeling the BCR to cost ratio in the broiler production.